| Project/Area Number |
17K14273
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Particle/Nuclear/Cosmic ray/Astro physics
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| Research Institution | Kyoto University (2018-2020)
The University of Tokyo (2017) |
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Principal Investigator |
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| Project Period (FY) |
2017-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2020: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2019: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2017: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | 宇宙の大規模構造 / 数値シミュレーション / 宇宙論パラメータ / 宇宙大規模構造 / 銀河統計 / 重力レンズ効果 / 摂動理論 / 多点統計量 / 宇宙論パラメタ / 宇宙物理 |
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| Outline of Final Research Achievements |
We studied the dynamics of the large-scale structure of the universe theoretically from various points of view, and the following results were obtained. First, the behavior of the response function describing the mode-coupling structure of fluctuations between different scales was numerically verified to give a physical interpretation. We also realized a hybrid approach that integrates theoretical predictions based on this function and also the propagator function with numerical simulations and machine learning. Based on the large-scale simulation database that we constructed, we designed and hosted the "Cosmology Challenge" program to allow robust tests of systematics in cosmological analyses of galaxy clustering. In addition to this, the possibility of cosmology using new probes such as consistency relations for multipoint correlation functions and correlation signals of galaxy shapes was tested and verified by numerical simulations.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究成果は、宇宙の大規模構造の背後に潜む物理的性質を炙り出すことを主目的としているが、同時に将来観測からアクセス可能な大規模精密データの解析に応用可能な理論テンプレートの形まで昇華することで、そのような観測計画の成功に欠かせない、基盤技術を提供するものである。これらの観測からは、ダークマター、ダークエネルギーの起源など、物理学のみならず一般から見ても注目度の高い、人類の宇宙観を形作る根源的課題の解決につながるものと期待できる。また、本研究成果は機械学習に物理的視点を加えることで、達成精度や解釈可能性を補完する可能性を実証しており、他分野に広く応用可能な汎用的手法へと繋がる可能性がある。
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